COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS
Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, [paleo]climatology, oceanography and biodiversity. In this work I present a comparative assessment of the datasets ETOPO1 (1’ resolution), GTOPO30, GLOBE, SRTM30 PLUS,...
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doaj-fb4c7d7c71da402682b173be21d878352020-11-25T02:31:38ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502016-06-01III-4172310.5194/isprs-annals-III-4-17-2016COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKSC. H. Grohmann0Institute of Energy and Environment, University of S˜ao Paulo, 05508-010, - S˜ao Paulo, SP, BrazilGlobal Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, [paleo]climatology, oceanography and biodiversity. In this work I present a comparative assessment of the datasets ETOPO1 (1’ resolution), GTOPO30, GLOBE, SRTM30 PLUS, GMTED2010 and ACE2 (30”) against the altitude of the world’s ultra prominent peaks. GDEMs’ elevations show an expected tendency of underestimating the peak’s altitude, but differences reach 3,500 m. None of the GDEMs captures the full range of elevation on Earth and they do not represent well the altitude of the most prominent peaks. Some of these problems could be addressed with the release of NASADEM, but the smoothing effect caused by moving-window resampling can only be tackled by using new techniques, such as scale-adaptative kernels and curvature-based terrain generalisation.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-4/17/2016/isprs-annals-III-4-17-2016.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. H. Grohmann |
spellingShingle |
C. H. Grohmann COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
C. H. Grohmann |
author_sort |
C. H. Grohmann |
title |
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS |
title_short |
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS |
title_full |
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS |
title_fullStr |
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS |
title_full_unstemmed |
COMPARATIVE ANALYSIS OF GLOBAL DIGITAL ELEVATION MODELS AND ULTRA-PROMINENT MOUNTAIN PEAKS |
title_sort |
comparative analysis of global digital elevation models and ultra-prominent mountain peaks |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2016-06-01 |
description |
Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology,
[paleo]climatology, oceanography and biodiversity. In this work I present a comparative assessment of the datasets ETOPO1 (1’
resolution), GTOPO30, GLOBE, SRTM30 PLUS, GMTED2010 and ACE2 (30”) against the altitude of the world’s ultra prominent
peaks. GDEMs’ elevations show an expected tendency of underestimating the peak’s altitude, but differences reach 3,500 m. None of
the GDEMs captures the full range of elevation on Earth and they do not represent well the altitude of the most prominent peaks. Some
of these problems could be addressed with the release of NASADEM, but the smoothing effect caused by moving-window resampling
can only be tackled by using new techniques, such as scale-adaptative kernels and curvature-based terrain generalisation. |
url |
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-4/17/2016/isprs-annals-III-4-17-2016.pdf |
work_keys_str_mv |
AT chgrohmann comparativeanalysisofglobaldigitalelevationmodelsandultraprominentmountainpeaks |
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1724823144846327808 |